The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to systems and methods for reducing artifacts in MRI data.
Due to engineering limitations, the gradient fields used for spatial encoding in clinical magnetic resonance imaging (“MRI”) are never truly linear over the imaging field-of-view (“FOV”). As standard MRI signal models presume gradient linearity, reconstructed images exhibit geometric distortion unless gradient deviations are properly accounted for. Given a priori knowledge of the gradient field, geometric distortion due to gradient nonlinearity is typically corrected via image-domain interpolation. Although this retrospective approach, commonly termed gradient distortion correction or “GradWarp,” is straightforward, it does not explicitly account for the effects of finite sampling, undersampling, or noise, and may consequently degrade spatial resolution.
Although prospective correction has been considered in situations when gradients are intentionally distorted for encoding purposes, such as parallel imaging techniques using localized gradients (“PATLOC”) this approach has not been considered for the more common scenario where ideally linear gradients are not performing as desired.
Hence, given the above, there is a need for systems and methods for accurate and efficient correction of gradient nonlinearity in magnetic resonance imaging.